Square contingency tables are traditionally analyzed with a focus on the symmetric structure of the corresponding probability tables. We view probability tables as elements of a simplex equipped with the Aitchison geometry. This perspective allows us to present a novel approach to analyzing symmetric structure using a compositionally coherent framework. We present a geometric interpretation of quasi-symmetry as an e-flat subspace and introduce a new concept called geometric marginal homogeneity, which is also characterized as an e-flat structure. We prove that both quasi-symmetric tables and geometric marginal homogeneous tables form subspaces in the simplex, and demonstrate that the measure of skew-symmetry in Aitchison geometry can be orthogonally decomposed into measures of departure from quasi-symmetry and geometric marginal homogeneity. We illustrate the application and effectiveness of our proposed methodology using data on unaided distance vision from a sample of women.
Developing expertise in physics requires appropriate integration and assimilation of physics and mathematics. Instructors and students often describe physics courses in terms of their emphasis on conceptual and quantitative problem-solving. For example, they may argue that a course emphasizes primarily conceptual over quantitative problem-solving or may emphasize equally on both depending on instructional context and assessment design. In this study, we investigated how students and instructors across different levels of physics instruction perceive the roles and development of conceptual and quantitative problem-solving in student learning and expertise development. Using departmental surveys administered at the beginning and end of each semester, we collected both Likert-scale and open-ended responses from students enrolled in introductory, upper-level undergraduate and graduate physics courses. These surveys assessed students' self-perceived skills, preferences and perceptions of instrucots and course emphasis. To complement student perspectives, we conducted interviews with instructors using parallel questions adapted to reflect instructional goals and expectations. Our findings highlight patterns in how students and instructos prioritize conceptual and quantitative problem-solving across course levels, as well as alignment and misalignment between student and instructor perspectives.
This qualitative investigation explored lay constructions of autistic women, a group that has been invisible from the making of “autism,” as it is constructed by society. Thirty-one neurotypical adults living in Australia (aged 19–65 years) responded to a written vignette about a fictional autistic woman. We performed a constructionist thematic analysis and developed three themes that positioned autistic women as misunderstood, excluded, and othered. We interpreted the results through the lenses of dominant gender constructs, ableist discourse, and intersectionality theory. Our interpretation of the findings illuminates the prevailing influence of male-dominant discourses of autism, hegemonic ideals of womanhood, and medicalised understandings of disability that people draw upon to make sense of autistic women. This novel research underscores the need for more education and awareness about this group.
The digital landscape provides a dynamic platform for political discourse crucial for understanding shifts in public opinion and engagement especially under authoritarian governments This study examines YouTube user behavior during the Russian-Ukrainian war analyzing 2168 videos with over 36000 comments from January 2022 to February 2024 We observe distinct patterns of participation and gender dynamics that correlate with major political and military events Notably females were more active in antigovernment channels especially during peak conflict periods Contrary to assumptions about online engagement in authoritarian contexts our findings suggest a complex interplay where women emerge as pivotal digital communicators This highlights online platforms role in facilitating political expression under authoritarian regimes demonstrating its potential as a barometer for public sentiment.
We study intersections of exceptional curves on del Pezzo surfaces of degree 1, motivated by questions in arithmetic geometry. Outside characteristics 2 and 3, at most 10 exceptional curves can intersect in a point. We classify the different ways in which 10 exceptional curves can intersect, construct a new family of surfaces with 10 exceptional curves intersecting in a point, and discuss strategies for finding more such examples.
Marco Antonio Stranisci, Rossana Damiano, Enrico Mensa
et al.
Biographical event detection is a relevant task for the exploration and comparison of the ways in which people's lives are told and represented. In this sense, it may support several applications in digital humanities and in works aimed at exploring bias about minoritized groups. Despite that, there are no corpora and models specifically designed for this task. In this paper we fill this gap by presenting a new corpus annotated for biographical event detection. The corpus, which includes 20 Wikipedia biographies, was compared with five existing corpora to train a model for the biographical event detection task. The model was able to detect all mentions of the target-entity in a biography with an F-score of 0.808 and the entity-related events with an F-score of 0.859. Finally, the model was used for performing an analysis of biases about women and non-Western people in Wikipedia biographies.
Amirhossein Alvandi, Sedigheh Omidvar, Armin Hatefi
et al.
We consider the Bayesian estimation of the parameters of a finite mixture model from independent order statistics arising from imperfect ranked set sampling designs. As a cost-effective method, ranked set sampling enables us to incorporate easily attainable characteristics, as ranking information, into data collection and Bayesian estimation. To handle the special structure of the ranked set samples, we develop a Bayesian estimation approach exploiting the Expectation-Maximization (EM) algorithm in estimating the ranking parameters and Metropolis within Gibbs Sampling to estimate the parameters of the underlying mixture model. Our findings show that the proposed RSS-based Bayesian estimation method outperforms the commonly used Bayesian counterpart using simple random sampling. The developed method is finally applied to estimate the bone disorder status of women aged 50 and older.
The nascent but rapidly growing field of Quantum Information Science and Technology has led to an increased demand for skilled quantum workers and an opportunity to build a diverse workforce at the outset. In order to meet this demand and encourage women and underrepresented minorities in STEM to consider a career in QIST, we have developed a curriculum for introducing quantum computing to teachers and students at the high school level with no prerequisites. In 2022, this curriculum was delivered over the course of two one-week summer camps, one targeting teachers and another targeting students. Here, we present an overview of the objectives, curriculum, and activities, as well as results from the formal evaluation of both camps and the outlook for expanding QCaMP in future years.
A common paradigm for identifying semantic differences across social and temporal contexts is the use of static word embeddings and their distances. In particular, past work has compared embeddings against "semantic axes" that represent two opposing concepts. We extend this paradigm to BERT embeddings, and construct contextualized axes that mitigate the pitfall where antonyms have neighboring representations. We validate and demonstrate these axes on two people-centric datasets: occupations from Wikipedia, and multi-platform discussions in extremist, men's communities over fourteen years. In both studies, contextualized semantic axes can characterize differences among instances of the same word type. In the latter study, we show that references to women and the contexts around them have become more detestable over time.
Cervical cancer is one of the most common types of cancer found in females. It contributes to 6-29% of all cancers in women. It is caused by the Human Papilloma Virus (HPV). The 5-year survival chances of cervical cancer range from 17%-92% depending upon the stage at which it is detected. Early detection of this disease helps in better treatment and survival rate of the patient. Many deep learning algorithms are being used for the detection of cervical cancer these days. A special category of deep learning techniques known as Generative Adversarial Networks (GANs) are catching up with speed in the screening, detection, and classification of cervical cancer. In this work, we present a detailed analysis of the recent trends relating to the use of various GAN models, their applications, and the evaluation metrics used for their performance evaluation in the field of cervical cancer imaging.
Despite rapid growth in the data science workforce, people of color, women, those with disabilities, and others remain underrepresented in, underserved by, and sometimes excluded from the field. This pattern prevents equal opportunity for individuals, while also creating products and policies that perpetuate inequality. Thus, for statistics and data science educators of the next generation, accessibility and inclusion should be of utmost importance in our programs and courses. In this paper, we discuss how we developed an accessibility and inclusion framework, hence a structure for holding ourselves accountable to these principles, for the writing of a statistics textbook. We share our experiences in setting accessibility and inclusion goals, the tools we used to achieve these goals, and recommendations for other educators. We provide examples for instructors that can be implemented in their own courses.
Coronary artery disease (CAD) is a major cause of death and disability in developed countries. Although CAD mortality rates worldwide have declined over the past 4 decades, CAD remains responsible for approximately one-third or more of all deaths in individuals over age 35, and it has been estimated that nearly half of all middle-aged men and one-third of middle aged women in the United States will develop clinical CAD. The present paper attempts to check the applicability of Lotka's Law on South African publication on Coronary artery disease research. The study lights on Lotka's empirical law of scientific productivity, i.e., Inverse Square Law, to measure the scientific productivity of authors, to test Lotka's Exponent value and the K.S test for the fitness of Lotka's Law.
In this paper, an approach for hate speech detection against women in Arabic community on social media (e.g. Youtube) is proposed. In the literature, similar works have been presented for other languages such as English. However, to the best of our knowledge, not much work has been conducted in the Arabic language. A new hate speech corpus (Arabic\_fr\_en) is developed using three different annotators. For corpus validation, three different machine learning algorithms are used, including deep Convolutional Neural Network (CNN), long short-term memory (LSTM) network and Bi-directional LSTM (Bi-LSTM) network. Simulation results demonstrate the best performance of the CNN model, which achieved F1-score up to 86\% for the unbalanced corpus as compared to LSTM and Bi-LSTM.
The purpose of this study is to find evidence for supporting the hypothesis that language is the mirror of our thinking, our prejudices and cultural stereotypes. In this analysis, a questionnaire was administered to 537 people. The answers have been analysed to see if gender stereotypes were present such as the attribution of psychological and behavioural characteristics. In particular, the aim was to identify, if any, what are the stereotyped images, which emerge in defining the roles of men and women in modern society. Moreover, the results given can be a good starting point to understand if gender stereotypes, and the expectations they produce, can result in penalization or inequality. If so, the language and its use would create inherently a gender bias, which influences evaluations both in work settings both in everyday life.
Lisa A. Chalaguine, Anthony Hunter, Henry W. W. Potts
et al.
Much research in computational argumentation assumes that arguments and counterarguments can be obtained in some way. Yet, to improve and apply models of argument, we need methods for acquiring them. Current approaches include argument mining from text, hand coding of arguments by researchers, or generating arguments from knowledge bases. In this paper, we propose a new approach, which we call argument harvesting, that uses a chatbot to enter into a dialogue with a participant to get arguments and counterarguments from him or her. Because it is automated, the chatbot can be used repeatedly in many dialogues, and thereby it can generate a large corpus. We describe the architecture of the chatbot, provide methods for managing a corpus of arguments and counterarguments, and an evaluation of our approach in a case study concerning attitudes of women to participation in sport.
Gynecologic malignancies are a leading cause of death in women worldwide. Standard treatment for many primary and recurrent gynecologic cancer cases includes a combination of external beam radiation, followed by brachytherapy. Magnetic Resonance Imaging (MRI) is benefitial in diagnostic evaluation, in mapping the tumor location to tailor radiation dose, and in monitoring the tumor response to treatment. Initial studies of MR-guidance in gynecologic brachtherapy demonstrate the ability to optimize tumor coverage and reduce radiation dose to normal tissues, resulting in improved outcomes for patients. In this article we describe a methodology to aid applicator placement and treatment planning for 3 Tesla (3T) MR-guided brachytherapy that was developed specifically for gynecologic cancers. This has been used in 18 cases to date in the Advanced Multimodality Image Guided Operating suite at Brigham and Women's Hospital. It is comprised of state of the art methods for MR imaging, image analysis, and treatment planning. An MR sequence using 3D-balanced steady state free precession in a 3T MR scan was identified as the best sequence for catheter identification with ballooning artifact at the tip. 3D treatment planning was performed using MR images. Item in development include a software module designed to support virtual needle trajectory planning that includes probabilistic bias correction, graph based segmentation, and image registration algorithms. The results demonstrate that 3T MR has a role in gynecologic brachytherapy. These novel developments improve targeted treatment to the tumor while sparing the normal tissues.
With the widespread use of email, we now have access to unprecedented amounts of text that we ourselves have written. In this paper, we show how sentiment analysis can be used in tandem with effective visualizations to quantify and track emotions in many types of mail. We create a large word--emotion association lexicon by crowdsourcing, and use it to compare emotions in love letters, hate mail, and suicide notes. We show that there are marked differences across genders in how they use emotion words in work-place email. For example, women use many words from the joy--sadness axis, whereas men prefer terms from the fear--trust axis. Finally, we show visualizations that can help people track emotions in their emails.
We propose a model of two-way selection system. It appears in the processes like choosing a mate between men and women, making contracts between job hunters and recruiters, and trading between buyers and sellers. In this paper, we propose a model of two-way selection system, and present its analytic solution for the expectation of successful matching total and the regular pattern that the matching rate trends toward an inverse proportion to either the ratio between the two sides or the ratio of the state total to the smaller people number. The proposed model is verified by empirical data of the matchmaking fairs. Results indicate that the model well predicts this typical real-world two- way selection behavior to the bounded error extent, thus it is helpful for understanding the dynamics mechanism of the real-world two-way selection system.
Biomimetic nanotechnology is a prominent research area at the meeting place of life sciences with engineering and physics: it is a continuously growing field that deals with knowledge transfer from biology to nanotechnology. Biomimetic nanotechnology is a field that has the potential to substantially support successful mastering of major global challenges. The Millennium Project was commissioned by the United Nations Secretary-General in 2002 to develop a concrete action plan for the world to reverse the grinding poverty, hunger and disease affecting billions of people. It states 15 Global Challenges: sustainable development, water, population and resources, democratization, long-term perspectives, information technology, the rich-poor gap, health, capacity to decide, peace and conflict, status of women, transnational crime, energy, science and technology and global ethics. The possible contributions to master these challenges with the help of biomimetic nanotechnology will be discussed in detail.